Development of an Ensemble Model for Disease Prediction using Laboratory Tests

The Machine learning model learns from given input data and make decisions or predictions without requiring any type of explicit programming. Machine learning models can observe trends, gain knowledge from past mistakes, and gradually enhance their performance. The utilization of machine learning is increasing in daily life especially in medical field for diagnosing a disease. The incorporation of machine learning into the quickly changing healthcare landscape has become increasingly pivotal for enhancing diagnostic accuracy and personalized patient care. The study is all about the preparation of an ensemble model that is made for predicting various diseases, leveraging insights gleaned from a diverse array of laboratory tests. Three potent models, including `random forest, extreme gradient boost, and light gradient boost, are included in the ensemble model. The research highlights how machine learning is influencing daily experiences with healthcare in a larger way in addition to concentrating on the creation and assessment of an accurate disease prediction. The ensemble model developed intends to showcase the capabilities of machine learning in handling complex medical data and delivering reliable predictions.